672 lines
28 KiB
Python
672 lines
28 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/v4.37.0/src/transformers/models/qwen2/modeling_qwen2.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import math
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from typing import Optional, Tuple, Union, List
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import torch
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from torch.nn import CrossEntropyLoss
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from torch.nn.functional import scaled_dot_product_attention as sdpa
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from ipex_llm.transformers.models.common import merge_qkv_base
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from ipex_llm.transformers.models.utils import SILU, mlp_fusion_check
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache, \
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should_use_compresskv, is_enough_kv_cache_room_4_36, get_compresskv_attn_mask
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
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from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache, \
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DynamicCompressCache, DynamicCompressFp8Cache
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from ipex_llm.utils.common import invalidInputError
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, Qwen2MLP
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from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb, repeat_kv
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.cache_utils import Cache
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from transformers import logging
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logger = logging.get_logger(__name__)
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def qwen2_model_forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None, # for transformers >= 4.42
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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invalidInputError(False,
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"You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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invalidInputError(False,
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"You have to specify either decoder_input_ids or decoder_inputs_embeds")
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. "
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"Setting `use_cache=False`..."
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)
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use_cache = False
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past_key_values_length = 0
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# ipex-llm changes start
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# IPEX-LLM OPT: kv cache and quantize kv cache
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inputs = input_ids if input_ids is not None else inputs_embeds
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use_quantize_kv = (
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self.config.hidden_size != 3584 # disable quantize kv in specific model
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and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs,
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self.config.num_attention_heads//self.config.num_key_value_heads)
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)
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use_compress_kv = should_use_compresskv(inputs, inputs.shape[1]) or \
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isinstance(past_key_values, DynamicCompressCache)
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if use_cache:
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if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
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if use_quantize_kv:
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past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
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else:
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past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
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elif use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
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DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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if not use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
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DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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past_key_values_length = past_key_values.get_usable_length(seq_length)
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# ipex-llm changes end
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length,
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dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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flash_attn_2 = self._attn_implementation == "flash_attention_2"
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if attention_mask is not None and flash_attn_2 and use_cache:
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is_padding_right = attention_mask[:, -1].sum().item() != batch_size
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if is_padding_right:
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invalidInputError(
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False,
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"You are attempting to perform batched generation with padding_side='right'"
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" this may lead to unexpected behaviour for Flash Attention version of Qwen2."
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" Make sure to call `tokenizer.padding_side = 'left'` before tokenizing "
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"the input. "
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)
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask_for_sdpa
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask
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# ipex-llm changes start: don't generate `attention_mask` in decode phase
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if seq_length == 1:
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attention_mask = None
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# ipex-llm changes end
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elif self._attn_implementation == "flash_attention_2":
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# 2d mask is passed through the layers
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attention_mask = attention_mask if (attention_mask is not None and
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0 in attention_mask) else None
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elif self._attn_implementation == "sdpa" and not output_attentions:
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# output_attentions=True can not be supported when using SDPA, and we fall back on
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# the manual implementation that requires a 4D causal mask in all cases.
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attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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)
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else:
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# 4d mask is passed through the layers
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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sliding_window=self.config.sliding_window,
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)
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hidden_states = inputs_embeds
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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attention_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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)
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else:
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# ipex-llm changes
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curr_device = decoder_layer.input_layernorm.weight.device
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if attention_mask is not None:
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attention_mask = attention_mask.to(curr_device)
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if position_ids is not None:
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position_ids = position_ids.to(curr_device)
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# ipex-llm changes end
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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# ipex-llm changes start: remove `to_legacy_cache`
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next_cache = None
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if use_cache:
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next_cache = next_decoder_cache
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# ipex-llm changes end
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache,
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all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def qwen2_model_forward_4_42(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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invalidInputError(
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(input_ids is None) ^ (inputs_embeds is None),
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"You cannot specify both input_ids and inputs_embeds at the same time, "
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"and must specify either one"
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)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. "
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"Setting `use_cache=False`..."
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)
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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# ipex-llm changes start
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# IPEX-LLM OPT: kv cache and quantize kv cache
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use_quantize_kv = (
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self.config.hidden_size != 3584 # disable quantize kv in specific model
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and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs_embeds,
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self.config.num_attention_heads//self.config.num_key_value_heads)
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)
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use_compress_kv = should_use_compresskv(inputs_embeds, inputs_embeds.shape[1]) or \
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isinstance(past_key_values, DynamicCompressCache)
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if use_cache:
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if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
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if use_quantize_kv:
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past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
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else:
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past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
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elif use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
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DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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if not use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
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DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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# ipex-llm changes end
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
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)
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hidden_states = inputs_embeds
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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causal_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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cache_position,
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)
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else:
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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# ipex-llm changes start: remove `to_legacy_cache`
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next_cache = None
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if use_cache:
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next_cache = next_decoder_cache
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# ipex-llm changes end
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache,
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all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def qwen2_causal_lm_forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None, # for transformers >= 4.42
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) -> Union[Tuple, CausalLMOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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# ipex-llm changes start: remove `logits.float()` to reduce memory usage with long input
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# logits = logits.float()
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# ipex-llm changes end
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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shift_labels = shift_labels.view(-1)
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|
# Enable model parallelism
|
|
shift_labels = shift_labels.to(shift_logits.device)
|
|
loss = loss_fct(shift_logits, shift_labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
def merge_qkv(module: torch.nn.Module):
|
|
merge_qkv_base(module, Qwen2Attention)
|
|
if isinstance(module, Qwen2Attention) and os.environ.get("IPEX_LLM_LOW_MEM", None) == "1":
|
|
del module.rotary_emb.cos_cached
|
|
del module.rotary_emb.sin_cached
|
|
|
|
|
|
def padding_mlp(module: torch.nn.Module):
|
|
# for qwen 1.5 14B
|
|
if isinstance(module, Qwen2MLP):
|
|
hidden_size = module.gate_proj.weight.shape[1]
|
|
intermediate_size = module.gate_proj.weight.shape[0]
|
|
padding_intermediate_size = (intermediate_size + 256 - 1) // 256 * 256
|
|
if intermediate_size % 256 == 0:
|
|
return
|
|
|
|
gate_weight = module.gate_proj.weight.data
|
|
new_gate_weight = torch.zeros([padding_intermediate_size, hidden_size],
|
|
dtype=gate_weight.dtype, device=gate_weight.device)
|
|
new_gate_weight[:intermediate_size, :] = gate_weight
|
|
if hasattr(module.gate_proj, 'out_features'):
|
|
module.gate_proj.out_features = padding_intermediate_size
|
|
module.gate_proj.weight = torch.nn.Parameter(new_gate_weight, requires_grad=False)
|
|
|
|
up_weight = module.up_proj.weight.data
|
|
new_up_weight = torch.zeros([padding_intermediate_size, hidden_size],
|
|
dtype=up_weight.dtype, device=up_weight.device)
|
|
new_up_weight[:intermediate_size, :] = up_weight
|
|
if hasattr(module.gate_proj, 'out_features'):
|
|
module.up_proj.out_features = padding_intermediate_size
|
|
module.up_proj.weight = torch.nn.Parameter(new_up_weight, requires_grad=False)
|
|
|
|
down_weight = module.down_proj.weight.data
|
|
new_down_weight = torch.zeros([hidden_size, padding_intermediate_size],
|
|
dtype=down_weight.dtype, device=down_weight.device)
|
|
new_down_weight[:, :intermediate_size] = down_weight
|
|
if hasattr(module.gate_proj, 'out_features'):
|
|
module.down_proj.in_features = padding_intermediate_size
|
|
module.down_proj.weight = torch.nn.Parameter(new_down_weight, requires_grad=False)
|
|
|
|
|
|
def qwen2_attention_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, _ = hidden_states.size()
|
|
device = hidden_states.device
|
|
|
|
# [CompressKV]
|
|
from ipex_llm.transformers.kv import DynamicCompressCache
|
|
use_compresskv = isinstance(past_key_value, DynamicCompressCache)
|
|
use_quantizekv = isinstance(past_key_value, DynamicFp8Cache)
|
|
|
|
if hasattr(self, 'qkv_proj') and self.qkv_proj is not None:
|
|
qkv = self.qkv_proj(hidden_states)
|
|
qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
|
|
qkv = qkv.transpose(1, 2)
|
|
query_states, key_states, value_states = qkv.split([self.num_heads,
|
|
self.num_key_value_heads,
|
|
self.num_key_value_heads], dim=1)
|
|
else:
|
|
# when quant_method is 'gptq'
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) \
|
|
.transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) \
|
|
.transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask[:, :, :, :kv_seq_len]
|
|
|
|
if should_use_fuse_rope(hidden_states, position_ids, self.training):
|
|
import xe_addons
|
|
xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
|
|
query_states, key_states)
|
|
else:
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
cos, sin = cos.to(device), sin.to(device)
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
|
cos, sin, position_ids)
|
|
|
|
if past_key_value is not None:
|
|
# [CompressKV]
|
|
if use_compresskv:
|
|
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx,
|
|
q_len)
|
|
key_states, value_states = past_key_value.update(
|
|
key_states, value_states, self.layer_idx,
|
|
query_states, attention_mask, self.num_key_value_groups,
|
|
self.config, enough_kv_room, 256)
|
|
else:
|
|
key_states, value_states = past_key_value.update(key_states, value_states,
|
|
self.layer_idx, None)
|
|
|
|
attn_weights = None
|
|
if query_states.device.type == "cpu":
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
attn_output = sdpa(query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=attention_mask,
|
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
is_causal=self.is_causal and attention_mask is None and q_len > 1)
|
|
elif not self.training and not hidden_states.requires_grad and \
|
|
use_flash_attention(query_states, key_states, attention_mask):
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
attn_output = sdpa(query_states.to(device, dtype=torch.float16),
|
|
key_states.to(device, dtype=torch.float16),
|
|
value_states.to(device, dtype=torch.float16),
|
|
is_causal=True).to(hidden_states.dtype)
|
|
elif use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
|
import xe_addons
|
|
if use_compresskv:
|
|
attention_mask = get_compresskv_attn_mask(key_states, attention_mask)
|
|
if use_quantizekv:
|
|
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
|
|
attention_mask)
|
|
else:
|
|
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
|
attention_mask)
|
|
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
|
|
import xe_addons
|
|
if use_quantizekv:
|
|
attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
|
|
value_states, attention_mask)
|
|
else:
|
|
attn_output = xe_addons.sdp_causal(query_states, key_states,
|
|
value_states, attention_mask)
|
|
else:
|
|
if use_quantizekv:
|
|
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
attn_weights = torch.matmul(query_states,
|
|
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
if attention_mask is not None:
|
|
attn_weights = attn_weights + attention_mask
|
|
# upcast attention to fp32
|
|
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
|
|
dtype=torch.float32).to(query_states.dtype)
|
|
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
|
|
training=self.training)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
def qwen2_mlp_forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
x_2d = x.view(-1, x.shape[-1])
|
|
qtype = getattr(self.gate_proj, "qtype", None)
|
|
if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
|
|
import xe_linear
|
|
return self.down_proj(xe_linear.mlp_forward_xpu(
|
|
x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
|
|
x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
|
|
SILU, qtype
|
|
))
|
|
elif x.device.type == "xpu" and not self.training:
|
|
import xe_addons
|
|
gate = self.gate_proj(x)
|
|
up = self.up_proj(x)
|
|
xe_addons.mlp_silu_mul_inplaced(gate, up)
|
|
return self.down_proj(gate)
|
|
else:
|
|
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|